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The code of OCCO-infrared and visible image fusion

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<h1 align="left">[IJCV25]-OCCO-Infrared and Visible Image Fusion</h1>

Implementation of our work:

Hui Li\*, Congcong Bian, Zeyang Zhang, Xiaoning Song, Xi Li, and Xiao-jun Wu<sup>📭</sup>, "**OCCO: LVM-guided Infrared and Visible Image Fusion Framework based on Object-aware and Contextual COntrastive Learning**", International Journal of Computer Vision (**IJCV**), 2025.

#### [[Paper](https://doi.org/10.1007/s11263-025-02507-2)]    [[Arxiv](https://arxiv.org/pdf/2503.18635)]

## Installation

Clone repo:
```
git clone https://github.com/bociic/OCCO.git
cd OCCO
```

The code is tested with Python == 3.9, PyTorch == 2.1.0 and CUDA == 12.1 on NVIDIA GeForce RTX 4090D, you may use a different version according to your GPU. 
```
conda create -n occo python=3.9
conda activate occo
pip install -r requirements.txt
```

## Quick Test

You can download the pre-trained weights from [occo](https://drive.google.com/file/d/1wJc6ureLgVD9BKwqfDRSSluLIn8DBmm0/view?usp=sharing) and place them in the ./logs/.

```
python main.py \
--test --use_gpu \    
--test_vis ./path/to/VIS \
--test_ir ./path/to/IR 
```
To work with your own test set, make sure to use the same file names for each infrared-visible image pair if you prefer not to edit the code.

## Training-Datasets

Organize the dataset folder in the following structure:

You can download the masks generated by [Grounding-DINO](https://github.com/IDEA-Research/GroundingDINO) and [SAM](https://github.com/facebookresearch/segment-anything) from here: [FMB](https://drive.google.com/drive/folders/1r-pOfG6On7et-r3Tb5DT2HyUiocg01K9?usp=sharing) and [MSRS](https://drive.google.com/drive/folders/1qPG4H1QEOsq1MmmS-h7oqHpOBim6Zy6g?usp=sharing). Alternatively, you can generate your own masks, but please ensure that the final masks are binary masks.

## Training

```
python main.py --train --epoch 30 --bs 30 --logdir <checkpoint_path> --dataset /path/to/ir/ --use_gpu
```
Please note that **dataset** points to the **infrared image folder**, not the entire dataset folder.

## Contact

If you have any questions about the code, please email us or open an issue,

Hui Li(`lihui.cv@jiangnan.edu.cn`) or Congcong Bian(`bociic_jnu_cv@163.com`).

## Citation

If you find this paper/code helpful, please consider citing us:

```
@article{Li2025,
  author = {Li, Hui and Bian, Congcong and Zhang, Zeyang and Song, Xiaoning and Li, Xi and Wu, Xiao-Jun},
  title = {OCCO: LVM-Guided Infrared and Visible Image Fusion Framework Based on Object-Aware and Contextual Contrastive Learning},
  journal = {International Journal of Computer Vision},
  year = {2025},
  issn = {1573-1405},
}
```

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